Showing that the race model inequality is not violated

When participants are asked to respond in the same way to stimuli from different sources (e.g., auditory and visual), responses are often observed to be substantially faster when both stimuli are presented simultaneously (redundancy gain). Different models account for this effect, the two most important being race models and coactivation models. Redundancy gains consistent with the race model have an upper limit, however, which is given by the well-known race model inequality (Miller, 1982). A number of statistical tests have been proposed for testing the race model inequality in single participants and groups of participants. All of these tests use the race model as the null hypothesis, and rejection of the null hypothesis is considered evidence in favor of coactivation. We introduce a statistical test in which the race model prediction is the alternative hypothesis. This test controls the Type I error if a theory predicts that the race model prediction holds in a given experimental condition.     

Diagnosticity and the base-rate effect

A common judgmental task involves predicting the category membership of an individual on the basis of information specific to that individual and background information regarding the base rate of different categories. According to statistical theory, predictions may deviate from base rates only to the extent that the individuating information is diagnostic. Previous research has demonstrated that diagnosticity is often judged by “representativeness,” the degree to which the individuating information is differentially suggestive of the different possible categories. Thus, information with high differential representativeness will swamp base-rate information even if it is almost worthless (e.g., because its source is unreliable). The present studies varied differential representativeness by manipulating the prediction categories’ similarity to one another vis-a-vis the individuating information. It was found that the effect of the base rate increased systematically as differential representativeness decreased. Representativeness was measured independently by several converging techniques. These measures predicted the magnitude of the base-rate effect, supporting the hypothesis that neutral stimuli are assigned to categories in proportion to the base rates.     

Testing the race inequality: A simple correction procedure for fast guesses

In speeded response tasks with redundant signals, parallel processing of the redundant signals is generally tested using the so-called race inequality. The race inequality states that the distribution of fast responses for a redundant stimulus never exceeds the summed distributions of fast responses for the single stimuli. It has been pointed out that fast guesses (e.g. anticipatory responses) interfere with this test, and a correction procedure (‘kill-the-twin’ procedure) has been suggested. In this note we formally derive this procedure and extend it to the case in which redundant stimuli are presented with onset asynchrony. We demonstrate how the kill-the-twin procedure is used in a statistical test of the race model prediction.     

The Power to Detect and Predict Individual Differences in Intra-Individual Variability Using the Mixed-Effects Location-Scale Model

Our goal is to provide empirical scientists with practical tools and advice with which to test hypotheses related to individual differences in intra-individual variability using the mixed-effects location-scale model. To that end, we evaluate Type I error rates and power to detect and predict individual differences in intra-individual variability using this model and provide empirically-based guidelines for building scale models that include random and/or systematically-varying fixed effects. We also provide two power simulation programs that allow researchers to conduct a priori empirical power analyses. Our results aligned with statistical power theory, in that, greater power was observed for designs with more individuals, more repeated occasions, greater proportions of variance available to be explained, and larger effect sizes. In addition, our results indicated that Type I error rates were acceptable in situations when individual differences in intra-individual variability were not initially detectable as well as when the scale-model individual-level predictor explained all initially detectable individual differences in intra-individual variability. We conclude our paper by providing study design and model building advice for those interested in using the mixed-effects location-scale model in practice.     

Explicit awareness supports conditional visual search in the retrieval guidance paradigm

In four experiments we explored whether participants would be able to use probabilistic prompts to simplify perceptually demanding visual search in a task we call the retrieval guidance paradigm. On each trial a memory prompt appeared prior to (and during) the search task and the diagnosticity of the prompt(s) was manipulated to provide complete, partial, or non-diagnostic information regarding the target's color on each trial (Experiments 1–3). In Experiment 1 we found that the more diagnostic prompts was associated with faster visual search performance. However, similar visual search behavior was observed in Experiment 2 when the diagnosticity of the prompts was eliminated, suggesting that participants in Experiment 1 were merely relying on base rate information to guide search and were not utilizing the prompts. In Experiment 3 participants were informed of the relationship between the prompts and the color of the target and this was associated with faster search performance relative to Experiment 1, suggesting that the participants were using the prompts to guide search. Additionally, in Experiment 3 a knowledge test was implemented and performance in this task was associated with qualitative differences in search behavior such that participants that were able to name the color(s) most associated with the prompts were faster to find the target than participants who were unable to do so. However, in Experiments 1–3 diagnosticity of the memory prompt was manipulated via base rate information, making it possible that participants were merely relying on base rate information to inform search in Experiment 3. In Experiment 4 we manipulated diagnosticity of the prompts without manipulating base rate information and found a similar pattern of results as Experiment 3. Together, the results emphasize the importance of base rate and diagnosticity information in visual search behavior. In the General discussion section we explore how a recent computational model of hypothesis generation (HyGene; Thomas, Dougherty, Sprenger, & Harbison, 2008), linking attention with long-term and working memory, accounts for the present results and provides a useful framework of cued recall visual search.     

Biomarkers, health, lifestyle, and demographic variables as correlates of reaction time performance in early, middle, and late adulthood

We aimed to identify demographic, health, and biomarker correlates of reaction time performance and to determine whether biomarkers explained age differences in reaction time performance. The sample comprised three representative cohorts aged 20-24, 40-44, and 60-64 years, including a total of 7,485 participants. Reaction time measures of intraindividual variability and latency were used. The measure of intraindividual variability used was independent of mean reaction time. Older adults were more variable than younger adults in choice reaction time performance but not simple reaction time performance. The most important correlates of reaction time performance after gender and education were biological markers such as forced expiratory volume at one second, grip strength, and vision. Few measures of physical or mental health or lifestyle were associated with poorer performance on reaction time measures. Biomarkers explained the majority of age-related variance in simple reaction time and a large proportion of variance in choice reaction time. We conclude that for the ages studied, biomarkers are more important than health factors for explaining age differences in reaction time performance.     

Testing the race model inequality: An algorithm and computer programs

In divided-attention tasks, responses are faster when two target stimuli are presented, and thus one is redundant, than when only a single target stimulus is presented. Raab (1962) suggested an account of this redundant-targets effect in terms of a race model in which the response to redundant target stimuli is initiated by the faster of two separate target detection processes. Such models make a prediction about the probability distributions of reaction times that is often called the race model inequality, and it is often of interest to test this prediction. In this article, we describe a precise algorithm that can be used to test the race model inequality and present MATLAB routines and a Pascal program that implement this algorithm.     

What's behind different kinds of kinds: effects of statistical density on learning and representation of categories

This research examined how differences in category structure affect category learning and category representation across points of development. The authors specifically focused on category density--or the proportion of category-relevant variance to the total variance. Results of Experiments 1-3 showed a clear dissociation between dense and sparse categories: Whereas dense categories were readily learned without supervision, learning of sparse categories required supervision. There were also developmental differences in how statistical density affected category representation. Although children represented both dense and sparse categories on the basis of the overall similarity (Experiment 4A), adults represented dense categories on the basis of similarity and represented sparse categories on the basis of the inclusion rule (Experiment 4B). The results support the notion that statistical structure interacts with the learning regime in their effects on category learning. In addition, these results elucidate important developmental differences in how categories are represented, which presents interesting challenges for theories of categorization.     

Redundancy gains and coactivation with two different targets: The problem of target preferences and the effects of display frequency

When a visual display contains two targets, both of which require the same response, reaction times (RTs) are faster than when only one target appears. This effect has previously been obtained regardless of whether the redundant targets are the same or different in shape, and in at least one set of two-target experiments, the redundancy gains have been larger for different targets (Grice & Reed, 1992). Experiments with two different targets have also revealed violations of the race-model inequality, suggesting that redundant targets coactivate the response (Miller, 1982). The present paper reexamines both of these findings, because both appear to be inconsistent with the interactive race model (Mordkoff & Yantis, 1991). Experiment 1 shows that the race-model inequality is not violated when the experimental design is free of biased contingencies; Experiment 1 also provides evidence that target preferences may artifactually produce the RT advantage fordifferent- oversame-target trials. Experiment 2, however, shows that the race-model inequality is violated when the frequencies of single- and redundant-target displays are equated (without introducing any biased contingencies), implying that the interactive race model cannot account for the results of experiments involving more than one type of target. Alternative loci for coactivation are briefly discussed.     

Predicting school achievement from cognitive and non-cognitive variables in a Chinese sample of elementary school children

The present study had two aims: First, to investigate the joint and specific roles of working memory (WM) and intelligence as predictors of school achievement. And second, to replicate and extend earlier findings (Spinath, Spinath, Harlaar, & Plomin, 2006) on the incremental validity of non-cognitive over cognitive abilities in the prediction of school achievement. The present sample consisted of N = 179 Chinese primary school children in the fourth grade. All measures including working memory (WM), intelligence and motivational items were assessed in class. Teachers provided test scores for the domains of Chinese and Math. We found that WM was a good predictor of school achievement and comparable in predictive power to intelligence. Together, cognitive ability including both WM and intelligence explained 17.8% and 36.4% of the variance in children's Chinese and Math scores, respectively. The relative importance of WM and intelligence varied with school domains with greater predictive power of WM for Math while intelligence explained a greater proportion of the variance in Chinese although the magnitude of this difference was only moderate. Domain-specific motivational constructs contributed only marginally to the prediction of school achievement for both Chinese and Math.     

Predictors of Depression and Anxiety Among International Students

The role of gender, age, race/ethnicity, length of stay, social support, and proficiency in English in the variance in depression and anxiety among international students revealed that social support was a significant predictor of depression and anxiety among international students. Age significantly contributed to the variance in anxiety, and self‐rated English proficiency uniquely contributed to the variance in both depression and anxiety. Latino/a students had significantly higher levels of depression than did Asian students.     

类别使用的特征诊断效应

运用预备实验评定过的鱼轮廓图,考察了类别判断和特征预测这两种类别使用方式下的特征诊断效应。被试是48名大学生,学习材料是两类鱼的类别成员,在测验阶段,实验1判断新项目类别标签,实验2预测新项目的缺失特征。结果表明,类别判断条件下,单个特征诊断力加强和诊断性特征数量增加都对类别判断有促进作用;特征预测条件下,只有单个特征诊断力加强可以促进特征预测,诊断性特征数量增加无助于特征预测     

COMMONALITY ANALYSIS: A METHOD FOR DECOMPOSING EXPLAINED VARIANCE IN MULTIPLE REGRESSION ANALYSES

Commonality analysis is a procedure for decomposing R² in multiple regression analyses into the percent of variance in the dependent variable associated with each independent variable uniquely, and the proportion of explained variance associated with the common effects of predictors. Commonality analysis thus sheds additional light on the magnitude of an obtained multivariate relationship by identifying the relative importance of all independent variables, findings which can be of theoretical and practical significance. In this paper we offer a brief explication of commonality analysis; a step-by-step discussion of how communication researchers may perform commonality analyses using output from computer-assisted statistical analysis programs like SPSS; and we provide an extended example illustrating a commonality analysis.     

Prime diagnosticity in short-term repetition priming: is primed evidence discounted, even when it reliably indicates the correct answer?

The authors conducted 4 repetition priming experiments that manipulated prime duration and prime diagnosticity in a visual forced-choice perceptual identification task. The strength and direction of prime diagnosticity produced marked effects on identification accuracy, but those effects were resistant to subsequent changes of diagnosticity. Participants learned to associate different diagnosticities with primes of different durations but not with primes presented in different colors. Regardless of prime diagnosticity, preference for a primed alternative covaried negatively with prime duration, suggesting that even for diagnostic primes, evidence discounting remains an important factor. A computational model, with the assumption that adaptation to the statistics of the experiment modulates the level of evidence discounting, accounted for these results.     

Explained Variance and Intraclass Correlation in a Two-Level AR(1) Model

The total variance of a first-order autoregressive AR(1) time series is well known in time series literature. However, despite the increased use and interest in two-level AR(1) models, an equation for the total variance of these models does not exist. This paper presents an approximation of this total variance. It will be used to compute the unexplained and explained variance at each level of the model, the proportion of explained variance, and the intraclass correlation (ICC). The use of these variances and the ICC will be illustrated using an example concerning structured diary data about the positive affect of 96 married women.     

Heterogeneous heterogeneity by default: Testing categorical moderators in mixed-effects meta-analysis

Categorical moderators are often included in mixed-effects meta-analysis to explain heterogeneity in effect sizes. An assumption in tests of categorical moderator effects is that of a constant between-study variance across all levels of the moderator. Although it rarely receives serious thought, there can be statistical ramifications to upholding this assumption. We propose that researchers should instead default to assuming unequal between-study variances when analysing categorical moderators. To achieve this, we suggest using a mixed-effects location-scale model (MELSM) to allow group-specific estimates for the between-study variance. In two extensive simulation studies, we show that in terms of Type I error and statistical power, little is lost by using the MELSM for moderator tests, but there can be serious costs when an equal variance mixed-effects model (MEM) is used. Most notably, in scenarios with balanced sample sizes or equal between-study variance, the Type I error and power rates are nearly identical between the MEM and the MELSM. On the other hand, with imbalanced sample sizes and unequal variances, the Type I error rate under the MEM can be grossly inflated or overly conservative, whereas the MELSM does comparatively well in controlling the Type I error across the majority of cases. A notable exception where the MELSM did not clearly outperform the MEM was in the case of few studies (e.g., 5). With respect to power, the MELSM had similar or higher power than the MEM in conditions where the latter produced non-inflated Type 1 error rates. Together, our results support the idea that assuming unequal between-study variances is preferred as a default strategy when testing categorical moderators.     

Fitting the ratcliff diffusion model to experimental data

Many experiments in psychology yield both reaction time and accuracy data. However, no off-the-shelf methods yet exist for the statistical analysis of such data. One particularly successful model has been the diffusion process, but using it is difficult in practice because of numerical, statistical, and software problems. We present a general method for performing diffusion model analyses on experimental data. By implementing design matrices, a wide range of across-condition restrictions can be imposed on model parameters, in a flexible way. It becomes possible to fit models with parameters regressed onto predictors. Moreover, data analytical tools are discussed that can be used to handle various types of outliers and contaminants. We briefly present an easy-touse software tool that helps perform diffusion model analyses.